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Record W4403808984 · doi:10.1093/eurheartj/ehae666.606

Predictors and outcomes of intracranial hemorrhage in 58,000 patients with atrial fibrillation on oral anticoagulation: insights from COMBINE-AF

2024· article· en· W4403808984 on OpenAlexaff
Manasi Tannu, Keqin Hua, Graeme J. Hankey, John H. Alexander, Robert P. Giugliano, John W. Eikelboom, Renato D. Lópes, L Wallentin, Christian T. Ruff, J Oldgren, Manesh R. Patel, W. Schuyler Jones, Kenneth W. Mahaffey, Anthony Carnicelli, Christopher B. Granger

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAtrial fibrillationCardiologyInternal medicineIntracranial HemorrhagesSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Abstract Background Among anticoagulated patients with atrial fibrillation (AF), intracranial hemorrhage (ICH) is the leading cause of bleeding-related death and disability. The COMBINE-AF database has patient-level data from four major randomized-controlled trials [ROCKET-AF (rivaroxaban), RE-LY (dabigatran), ENGAGE-TIMI 48 (edoxaban), ARISTOTLE (apixaban)] of direct oral anticoagulants (DOACs) versus warfarin in patients with AF. Purpose/Methods We investigated the rates, predictors, and mortality of ICH in patients randomized to DOAC (rivaroxaban, dabigatran, edoxaban, apixaban) or warfarin. ICH was subclassified as subdural or non-subdural (intracerebral, intraventricular, subarachnoid, epidural). Multivariable Cox models were used to identify predictors of ICH. Frequencies and fatality rates of ICH were calculated and displayed as Kaplan-Meier curves. Results In COMBINE-AF, there were 58,634 patients randomized to either DOAC (n=29,362) or warfarin (n=29,272). The mean age was 70.5 years, 37% were female, and 81% were White. The mean CHA2DS2-VASc score was 2.6, 43% of patients had a history of smoking, 31% had diabetes, and 29% had prior stroke/TIA. After a mean of 32 months, ICH occurred in 593 patients (1.01%, 0.58 per 100-patient years, 95% CI [0.54, 0.63]). The most significant independent predictors of ICH in order of importance were: treatment with warfarin, older age, prior stroke/TIA, enrollment in Asia, antiplatelet use at randomization, and history/risk of falls (Figure 1). Mortality after ICH was 48.4% (287/593 participants), with a majority of deaths (82%) occurring within 30 days of ICH event. Non-subdural ICH was more common than subdural ICH (73% vs 25%) and had a higher risk of death (Figure 2). Participants randomized to DOAC had a lower risk of both subdural and non-subdural ICH than those treated with warfarin; however, mortality rates after ICH were similar regardless of anticoagulant type. Conclusion ICH is an uncommon but devastating complication of oral anticoagulation in patients with atrial fibrillation. The mortality rate following ICH is high, particularly following non-subdural ICH. To reduce the risk of ICH, clinicians should consider using DOACs over warfarin and carefully evaluate the need for antiplatelet therapy, particularly in patients with other significant predictors of ICH such as prior stroke, advanced age, residence in Asia, and history of falls.Predictors of Intracranial Hemorrhage90-Day Mortality after ICH

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.293
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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